Methods and systems for characterizing aging in a subject

Abstract

Disclosed herein are systems and methods of characterizing aging in a subject, including: obtaining gene expression data collected from a plurality of samples from the subject; identifying a plurality of differentially expressed genes based on the gene expression data; determining an age signature of each sample in the plurality of samples based on the plurality of differentially expressed genes; characterizing aging in the subject based on the determined age signature of each sample in the plurality of samples.

Description

T002910Quarles 166118.01584 METHODS AND SYSTEMS FOR CHARACTERIZING AGING IN A SUBJECT CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 773,930, filed on March 18, 2025, which is incorporated herein by reference in its entirety for all purposes.BACKGROUND

[0002] Aging is linked to progressive deterioration of biological function in living systems, and may be influenced by a variety of factors such as genetic inheritance, environmental conditions, and behavioral choices. There exists a need to better characterize aging in a subject to improve insights into changes in gene expression and phenotypes associated with aging.SUMMARY

[0003] Disclosed herein are systems and methods of characterizing aging in a subject, including: obtaining gene expression data collected from a plurality of samples from the subject; identifying a plurality of differentially expressed genes based on the gene expression data; determining an age signature of each sample in the plurality of samples based on the plurality of differentially expressed genes; characterizing aging in the subject based on the determined age signature of each sample in the plurality of samples.BRIEF DESCRIPTION OF THE DRAWINGS

[0004] FIGS. 1A-1B show atavistic over-representation observed in the (FIG. 1A) GenAge and (FIG. IB) AgeMeta aging signatures. The figures illustrate gene expression changes in different cell types (skin, immune, and ovarian cells) across evolutionary age categories. The bar charts separate genes into upregulated (blue) and downregulated (orange) categories compared to the expected distribution (green), providing insight into how aging influences gene regulation across different phylogenetic layers. The star above a bar chart means that the overrepresentation test is significant (red for over-representation test, black for under-representation test, * = p < 0.05 with FDR-correction).

[0005] FIGS. 2A-2E show atavistic over-representation observed in the skin, immune and ovarian cells. The figures illustrate gene expression changes in different cell types ((FIG. 2A,T002910Quarles 166118.01584 FIG. 2B) skin, (FIG. 2C) immune, (FIG. 2D) ovarian cells, and (FIG. 2E) human progenitor cells) across evolutionary age categories. The bar charts separate genes into upregulated (blue) and downregulated (orange) categories compared to the expected distribution (green), providing insight into how aging influences gene regulation across different phylogenetic layers. The star above a bar chart means that the over-representation test is significant (red for overrepresentation test, black for under-representation test, * =p< 0.05 with FDR-correction).

[0006] FIGS. 3A-3B show atavistic over-representation found in the (FIG. 3A) senescent genetic signature and (FIG. 3B) mesenchymal-senescent cells. The figures illustrate gene expression changes in different cell types (skin, immune, and ovarian cells) across evolutionary age categories. The bar charts separate genes into upregulated (blue) and downregulated (orange) categories compared to the expected distribution (green), providing insight into how aging influences gene regulation across different phylogenetic layers. The star above a bar chart means that the over-representation test is significant (red for over-representation test, black for underrepresentation test, * = p < 0.05 (FDR-corrected]).

[0007] FIGS. 4A-4D show no atavistic genetic over-representation is found during aging in brain cells and mesenchymal stem cells. The brain cells include cells collected from the (FIG.4A) cerebellum, (FIG. 4B) hippocampus, and (FIG. 4C) cortex. The figures illustrate gene expression changes in different cell types (skin, immune, and ovarian cells) across evolutionary age categories. The bar charts separate genes into upregulated (blue) and downregulated (orange) categories compared to the expected distribution (green), providing insight into how aging influences gene regulation across different phylogenetic layers. The star above a bar chart means that the over-representation test is significant (red for over-representation test, black for underrepresentation test, * = p < 0.05 with FDR-correction).

[0008] FIGS. 5A-5M show cumulative percentage distributions of gene evolutionary ages for aging-related gene sets (blue lines) compared to the baseline genome distribution (orange lines). Red stars indicate significant over-representation and black stars indicate significant under-representation at each evolutionary age cutoff (FDR-corrected p < 0.05, cumulative hypergeometric test). Data is shown for (FIG. 5A) GenAge aging signature, (FIG. 5B) AgeMeta aging signature, (FIG. 5C) skin cells (40-69 years old), (FIG. 5D) skin cells, >70 years old, (FIG. 5E) CD+ T cells, (FIG. 5F) ovarian cells, (FIG. 5G) human progenitor cells,T002910Quarles 166118.01584 (FIG. 5H) mesenchymal senescent cells, (FIG. 51) CellAge cenescence signature, (FIG. 5J) brain cortex, (FIG. 5K) brain hippocampus, (FIG. 5L) brain cerebellum), and (FIG. 5M) mesenchymal stem cells.

[0009] FIG. 6 shows an example process in accordance with some embodiments of the methods described herein.

[0010] FIG. 7 shows an example system in accordance with some embodiments of the systems described herein.DETAILED DESCRIPTION

[0011] In accordance with some embodiments of the disclosed subject matter, mechanisms (which can include, for example, systems, and methods) for characterizing aging in a subject are provided herein.

[0012] In some embodiments, a method of characterizing aging in a subject may include: obtaining gene expression data collected from a plurality of samples from the subject; identifying a plurality of differentially expressed genes based on the gene expression data; determining an age signature of each sample in the plurality of samples based on the plurality of differentially expressed genes; characterizing aging in the subject based on the determined age signature of each sample in the plurality of samples.

[0013] A plurality of samples may be collected from a subject (e.g., a human, animal, plant, or synthetic multicellular organism). In some embodiments, each sample is collected from a specific tissue or cell type, including at least one of neural tissue, muscle tissue, connective tissue, skin cells, senescent cells, brain cells (including brain cells from specific brain areas), immune cells, ovarian cells, bone cells, or stem cells. The overall assessment of evolutionary age agreement can be done among organs in the body, among tissues within an appendage or organ system, or among individual cells within a sample. Samples can be ex vivo organs, biopsy samples, bioengineered structures, organs for transplantation, diagnostic samples collected from patients, cell culture, animals, plants, or synthetic multicellular organisms (e.g., biobots).

[0014] A plurality of differentially expressed genes in each sample may be determined. Differentially expressed genes are genes whose expression levels vary significantly acrossT002910Quarles 166118.01584 tissues, conditions, etc. In some embodiments, the identified differentially expressed genes may be protein-encoding genes.

[0015] An age signature of a gene relates the gene to an evolutionary age. For instance, an evolutionary age may relate the date of origin of specific genes by studying homologs across species, and determining the earliest date of origin of the gene. A gene may then have an evolutionary age associated with a specific phylostratigraphic category. An age signature of a sample is based on the plurality of differentially expressed genes. For instance, an age signature of a sample may be an average evolutionary age across differentially expressed genes within the sample. An age signature may include at least one transcriptomic signature of aging. An age signatures may alternatively be calculated from a biological clock such as an epigenetic clock.

[0016] Characterizing aging in the subject includes comparing the age signatures of each sample in the plurality of samples. For instance, characterizing aging may include determining the age of genes expressed in each sample, and calculating to what degree the samples all agree on the evolutionary age of the genes expressed in each sample. This may be referred to as determining “coordination of aging.” For instance, N different samples collected from a subject may be collected, analyzed, and an aging signature may be determined for each. Then, it can be assessed the degree to which the samples have a shared evolutionary age of the genes they express. If the evolutionary ages of the samples agree, the subject is likely to be healthy. If the evolutionary ages of the samples disagree (e.g., some evolutionary ages are older, some are younger), then there is likely a problem (e g., advanced aging or a disorder). In some embodiments, the most important changes are differences in the most ancient genes (e.g., under expression or overexpression of the most ancient genes may be a strong indicator of advanced aging or a disorder).

[0017] Characterizing aging may include determining a distribution of the age signature of each sample in the plurality of samples. Characterizing aging can further include determining a measure of the discordance (e.g., difference of spread) of age signatures of each sample. It may include determining a fraction or percentage of the samples that have a shared aging signature. Determining the spread may include determining a range, interquartile range, variance, standard deviation, or mean absolute deviation of the distribution of age signatures. In someT002910Quarles 166118.01584 embodiments, characterizing aging can further include identifying outliers in the distribution of age signatures of each sample.

[0018] Expression data refers to data that infers the expression of a gene, based on RNA expression or protein expression. The expression data may include RNA expression data and / or protein expression data.

[0019] The terms "polynucleotide" or "nucleic acid" are used interchangeably herein and refer to a polymeric form of nucleotides of any length, either ribonucleotides or deoxyribonucleotides. Thus, this term includes, but is not limited to, single-, double- or multistranded DNA or RNA, genomic DNA, DNA-RNA hybrids, or a polymer comprising purine and pyrimidine bases, or other natural, chemically or biochemically modified, non-natural, or derivatized nucleotide bases. These terms also refer to complementary DNA (cDNA), which is DNA synthesized from a single-stranded RNA (e.g., messenger RNA (mRNA) or microRNA (miRNA)) template in a reaction catalyzed by the enzyme reverse transcriptase. The backbone of the polynucleotide can comprise sugars and phosphate groups (as may typically be found in RNA or DNA) or modified or substituted sugar or phosphate groups.

[0020] As used herein, the term “encoding” refers to the inherent property of specific sequences of nucleotides in a polynucleotide, such as a gene, a cDNA, or an mRNA, to serve as templates for synthesis of other polymers and macromolecules in biological processes having either a defined sequence of nucleotides (i.e., rRNA, tRNA and mRNA) or a defined sequence of amino acids and the biological properties resulting therefrom. Thus, a gene encodes a protein if transcription and translation of mRNA corresponding to that gene produces the protein in a cell or other biological system. In some embodiments, mRNA may be used as a rough approximation for protein expression.

[0021] As used herein, the term “isolated” means that the material is removed from its original environment (e.g., the natural environment if it is naturally occurring). For example, a naturally occurring polynucleotide or polypeptide present in a living microorganism is not isolated, but the same polynucleotide or polypeptide, separated from some or all of the coexisting materials in the natural system, is isolated. Such polynucleotides could be part of a composition and still be isolated in that such composition is not part of its natural environment.T002910Quarles 166118.01584

[0022] In some embodiments, RNA expression data may include RNA-seq or scRNA-seq data. Any conventional methods of collecting RNA sequencing data may be used. RNA sequencing may produce results that provide expression levels for a number of genes present in the sample. The results of RNA sequencing may be provided to a computer system to process the data and implement the methods described herein.

[0023] In some embodiments, protein expression data may be used in addition to or instead of RNA sequencing data. The protein data may include expression levels of specific genes. Common methods, such as ELISA assays, may be used to determine protein expression levels. The results of the protein detection methods may be provided to a computer system to process the data and implement the methods described herein.

[0024] In some embodiments, characterizing aging in the sample further includes performing enrichment analysis on the identified plurality of differentially expressed genes. Enrichment analysis can quantify whether a specific aging signature or evolutionary age is overexpressed relative to a random subset of genes. Overexpression of genes with an older phylostratigrpahic category may be linked to advanced aging. Therefore, determining whether genes with a specific aging signature is overexpressed can help determine an overall characterization of aging in the sample. An “older” aging signature may be associated with an early phylostratigraphic category. A “younger” aging signature may be associated with a more recent phylostratigraphic category. If genes with an older aging signature are overexpressed, and genes with a younger aging signature are under expressed, the sample may be characterized as having advanced aging.

[0025] In some embodiments, enrichment analysis includes selecting a subset of genes with a shared aging signature and calculating the probability of observing the subset of genes with the shared aging signature relative to a randomly selected subset of genes. In some embodiments, the shared aging signature is a shared phylostratigraphic category.

[0026] Characterizing aging may further include comparing characterized aging relative to a control population of a plurality of control subjects. This may include determining a baseline distribution of aging signatures of the plurality of control subjects, which may function as a standard distribution of aging signatures. The plurality of control samples may be selected from a database for which aging analysis has been computed. Characterizing a shift in the age signatureT002910Quarles 166118.01584 determines whether a sample’s or subject’s aging signature falls within the expected distribution of aging signatures based on the control distribution, or if the sample’s aging signature is abnormal (e.g., falls outside the control distribution of aging signatures). In some embodiments, this may include comparing the aging signature of a single sample in the subject (e.g., a specific tissue sample). In some embodiments, this may include comparing the aging signature of the subject (e.g., all of the samples from the subject).

[0027] In some embodiments, assessing shifts in the age signature of a sample is based on cell type or tissue type. For instance, a baseline distribution of aging signatures may be determined across all tissue types. Then, a distribution of aging signatures for a specific tissue (e.g., the sample was collected from a specific tissue or cell type) can be determined and compared to the baseline distribution.

[0028] In some embodiments, assessing shifts in the age signature of the subject is based on age. For example, a baseline distribution of aging signatures may be determined across all ages. Then, a distribution of aging signatures for a specific age (e.g., the sample was collected from a subject with a specific age) can be determined and compared to the baseline distribution.

[0029] In some embodiments, assessing shifts in the age signature of the subject is based on species. For example, a baseline distribution of aging signature may be determined across a plurality of species (e.g., many species with shared characteristics, or many species in a genus). Then, a distribution of aging signatures for a specific species can be determined and compared to the baseline distribution.

[0030] In some embodiments, assessing shifts in the age signature of the subject is based on strains. For example, a baseline distribution of aging signatures may be determined for a plurality of strains within a species. Then, a distribution of aging signatures for a specific strain can be determined and compared to the baseline distribution.

[0031] In some embodiments, assessing shifts in the age signature of the subject is based on treatment condition. A treatment condition may include an intervention, such as exposure to a drug, exposure to low gravity, exposure to ultrasound, exposure to electrical stimulation, exposure to light, exposure to space travel, etc. A baseline distribution of aging signatures may be determined for a plurality of subjects that have not been exposed to the treatment condition.T002910Quarles 166118.01584 Then, the aging signature of a subject who has been exposed to the treatment condition can be compared to the baseline distribution.

[0032] In some embodiments, assessing shifts in the age signature of the subject is based on disease. A baseline distribution of aging signatures of healthy subjects may be determined. Then, the aging signature of the subject who has or is suspected of having a disease can be compared to the baseline distribution. This can help predict disease (e.g., whether or not the aging signature of the subject falls within the baseline distribution), or provide a measure of disease progression (e.g., how far outside of the baseline distribution a subject falls).

[0033] In another aspect, the systems and methods described herein may be used to predict a disease based on the characterized aging in the sample. In some embodiments, predicting a disease may be completed by comparing aging of different tissue samples from the same subject. Predicting a disease may include collecting at least two samples from a single subject (e.g., a first sample and a second sample), collected from different tissues (e.g., skin cells and ovarian cells). The systems and methods described herein may be used to characterize aging in each sample. Then, the characterized aging of the first and second sample may be compared with one another. If there is a significant difference in the evolutionary ages of the samples, there is likely a disorder or impending disorder in the subject. For instance, if a sample of ovarian cells have a significantly older characterized age than the skin cells, this may indicate that there is a disease in the ovaries.

[0034] In other embodiments, predicting a disease may be completed by determining a control age signature of a control population, and comparing the characterized aging of the control population and the characterized aging of the sample. A control population may include a plurality of healthy subjects (e.g., subjects who have not been diagnosed with a disease) with a similar age to the subject from which the sample was collected (e.g., within 5 years of age of the subject from which the sample was collected). In some embodiments, a specific aging signature (e.g., a specific evolutionary age of a certain sample, or a specific distribution of ages across samples) may be associated with a specific type of disease.

[0035] Predicting a disease may further include identifying genes of interest. Genes of interest may be known to be associated with a disease. The genes of interest may be part of a pathway that is known to be dysregulated when in a disease state. Additionally or alternatively,T002910Quarles 166118.01584 genes of interest may be genes that are significantly upregulated or down regulated compared to a control population or baseline distribution. Identifying genes of interest can help aid in predicting disease, and help aid in determining predictions for specific diseases (e.g., predict whether an immune disorder or metabolic disorder is more likely based on which genes are over or under expressed).

[0036] Diseases that may be predicted using the methods and systems described herein include but are not limited to diseases associated with aging, cardiovascular disease, cancer, neurodegenerative disorders, metabolic disorders, immune system diseases, dysautonomia, or postural orthostatic tachycardia syndrome (POTS).

[0037] In another aspect, the systems and methods described herein can be used for longitudinal monitoring of potential disease in a subject. For instance, an updated sample may be periodically (e.g., at a set time after the initial sample was collected) can be collected, and the methods may be used to characterize the aging of the updated sample. The characterized aging of the original sample and updated sample can be compared. Updated samples may be collected every month, two months, three months, six months, year, or two years. The method may include tracking changes in the characterized aging of the sample overtime.

[0038] In some embodiments, the systems and methods described herein can be implemented to aid in preparation of tissue or organ transplant. For instance, a sample may be collected from the subject in need of a transplant to determine a characterized age of the subject. An additional sample may be collected from a potential donor tissue or organ, and the systems and methods may be used to characterize the age of the potential donor tissue. Subjects may be more likely to respond well to receiving donor tissue with a similar characterized age.

[0039] In some embodiments, the systems and methods described herein can be implemented to aid in building or assessing synthetic multicellular organisms or biobots. For instance, samples of the synthetic multicellular organism may include a plurality of cells. The samples can be evaluated to determine if they share an aging signature. Synthetic multicellular organisms or biobots that have a shared aging signature across cells may function better than those that have differing age signatures across cells.

[0040] In some embodiments, the systems and methods described herein can be implemented to aid in evaluating the health of agricultural animals. For instance, the systems andT002910Quarles 166118.01584 methods may be used to characterize the aging in each animal in a population of agricultural animals. This can be used to determine which animals are likely healthy (e.g., have the same or similar aging signatures across samples) or likely unhealthy (e.g., have different aging signatures across samples). Animals that are likely unhealthy may be removed from the population as they are likely old, diseased, or have an impending disease.

[0041] In some embodiments, the systems and methods described herein can be implemented to aid in evaluating the health of plants, particularly agricultural crops. The age of each plant in a selection of plants can be determined. This can be used to determine which plants in a selection of plants are likely healthy (e.g., have the same or similar aging signatures across samples) or likely unhealthy (e.g., have a wide or varied distribution of aging signatures across samples).

[0042] In some embodiments, the systems and methods described herein can be implemented to aid in evaluating lab-grown food. In some embodiments, a “subject” may refer to one selection of lab-grown food may be analyzed and aging signatures for a plurality of cells in the lab-grown food may be determined and compared. In some embodiments, multiple “subjects” of lab-grown food selections may be analyzed to determine whether the different selections share an aging signature. In still other embodiments, the aging signature of a lab-grown food selection can be determined over time. Lab-grown food in which the aging signatures have wide or varied distributions of aging signatures across samples may be discarded as likely lower quality compared to lab-grown food selections in which the aging signatures are the same or similar across samples. Additionally or alternatively, aging signatures of lab-grown food can be compared to aging signatures of comparable non-lab-grown food.

[0043] In some embodiments, the systems and methods described herein can be used to aid in screening for the effects of a treatment condition. As described above, an aging signature of a subject that has been exposed to a treatment condition can be determined and compared to aging signatures of control subjects that have not been exposed to a treatment condition.Additionally or alternatively, the methods and systems may be used to determine an aging signature of the subject prior to exposure to the treatment condition, and after exposure to the treatment condition. The aging signatures can be compared to help determine the effects of the treatment. This can be used to screen drugs or other medical treatments.T002910Quarles 166118.01584

[0044] In some embodiments, the method may include outputting a report that provides the characterized aging of the sample. The report may include the aging signature of the identified genes, and may further identify genes that are over or under expressed.

[0045] Examples

[0046] Example 1: Meta-Phylostratigraphic Evidence of Cellular and Tissue-Level Phylogenetic Dissociation

[0047] Overview

[0048] Aging is commonly attributed to accumulated damage, or evolved antagonistic genetic trade-offs, which lead to an accumulation of damage causing misexpression of genes necessary for longevity. We propose an atavistic dysregulation of gene expression at cellular and tissue levels during aging, framing aging as a gradual regression toward ancestral cellular states. Similarly to the atavistic model of cancer, in which cells revert to unicellular-like behavior, aging may result from the breakdown of coordinated morphogenetic control, leading organs and tissues toward less integrated, ancient unicellular states. We suggest that aging may involve a progressive reversal of the well-known ontogenetic tracing of prior phylogenetic embryonic characteristics. Moreover, aging could involve a loss of large-scale coordination, with tissues reverting to ancient gene expression to different degrees. We tested this hypothesis using a meta-phylostratigraphic analysis, finding: (1) An atavistic over-representation of differential expression in the most ancient genes and under-representation in the evolutionary youngest genes for two multi-tissue aging databases, and tissues covering skin, ovarian, immune, senescent and mesenchymal-senescent cells; (2) No significant atavistic over-representation of the differential gene expression during aging of brain cells and mesenchymal stem cells; (3) overall age-dependent increase of heterogeneity in the direction of the phylogenetic position of tissues' transcriptional profiles; (4) and an overall negative evolutionary age mean shift toward the most ancient genes. Our analyses suggest that aging involves uncoordinated and tissuespecific phylogenetic changes in gene expression. Understanding aging as a structured, heterogeneous atavistic process opens new avenues for rejuvenation, focusing on restoring multicellular coherence in evolutionarily youthful gene expression.

[0049] Aging represents an atavistic over-representation of differential expression in the most ancient genes and under-representation in the evolutionary youngest genes for two multi-T002910Quarles 166118.01584 tissue aging databases, and tissues covering skin, ovarian, immune, senescent and mesenchymal-senescent cells. However, some tissues like brain and mesenchymal stem cells do not show significant atavistic shifts. Overall, aging reflects a tissue-specific, uncoordinated genetic atavistic dissociation, opening new perspectives for rejuvenation targeting multicellular integration and youthful gene expression patterns.

[0050] Introduction

[0051] Aging Theories

[0052] Most complex organisms experience a sequential life course consisting of embryonic development stages, growth to maturity, and then functional decline and ultimately death. Aging leads to progressive deterioration of cellular structures and biological functions in living systems. The aging process is influenced by diverse elements including genetic inheritance, environmental conditions, and behavioral choices. The resistance of biological systems to noise and damage decreases with age, leading to health issues including cardiovascular complications, cancer, neurodegenerative disorders, metabolic disruptions such as type II diabetes, and compromised immune responses to pathogens, which strongly impact quality of life.

[0053] The scientific community has been focused on two main theoretical frameworks to understand the aging process: damage-based and programmatic theories. Damage-based theories hold that aging results from the inevitable accumulation of molecular damage over time. Essential cellular components including genetic stability, telomere length, mitochondrial activity, and protein homeostasis experience severe degradation. In contrast, programmatic theories propose that aging is the evolutionary result of a genetically encoded biological trajectory. This perspective suggests that the aging process is encoded in our genome, because it provides a reproductive advantage at the lineage level, rather than merely representing stochastic damage accumulation over time.

[0054] Longevity as the Result of Anatomical Homeostasis

[0055] A novel direction with respect to aging is suggested by a focus on morphogenesis and morphostasis (cellular self-assembly into complex anatomical forms, and continued maintenance of the correct structure) as active navigation of anatomical morphospace. ThisT002910Quarles 166118.01584 framework sees embryogenesis, regeneration, and cancer suppression as a continuous dynamic set of decisions made by the cellular collective to reach and maintain organ-level target morphologies. Numerous tools from behavioral neuroscience and cybernetics have been deployed in morphogenetic systems to probe the mechanisms that coordinate cells toward common anatomical endpoints. The ability to reach and maintain species-specific large-scale anatomical states reliably, despite noise and perturbations, is a kind of homeostatic dynamic that has been modeled as a collective intelligence, with many applications in biomedicine across birth defects and regeneration. Moreover, this approach has provided novel ways to address cancer as a loss of coordination of cells toward tissue-level homeostatic goals. From this perspective, one can ask what kind of information-processing disorders could contribute to aging as a long-term loss of the ability to organize cells and subcellular materials toward maintenance of a healthy complex form.

[0056] We recently proposed that aging is the result of the loss of morphostatic information. In this framework, aging is an emergent failure mode of information-processing collectives that lose morphogenetic guidance after the construction of the adult body structure has been completed. This leads us to investigate what kind of directional changes might be observed in cells and tissues after the primary anatomical goals have been met, in addition to loss of precision and random alterations. Moreover, we can explore whether, given an anatomical setpoint when not bound to a single (body-wide) as occurs during embryogenesis, different cells in the body eventually adopt distinct setpoints, thus resulting in the loss of coherent function as observed in aging.

[0057] The setpoints for anatomical homeostasis in evolved creatures are shaped by evolution; specifically, they are determined by phylogenetic position and the body plan it specifies. One theory is that the kind of dissociation of self-model from reality that is observed in certain psychological disease states have a somatic counterpart, in which the actual phylogenetic age of cells ( Homo sapiens ) could become dissociated from the effective physiological phylogenetic age due to the expression of more ancient genes. We sought to explore the relationship between an organism's age and the phylogenetic information its tissues attempt to implement. Using transcriptional profiling datasets, and phylostratigraphy to analyze the evolutionary age of genes being expressed in specific tissues, we tested two hypotheses. First, that aging involves a roll-back of transcriptional profiles to include more ancient genes (in effect,T002910Quarles 166118.01584 a reversal of the well-known “ontogeny recapitulates phylogeny” dynamic with respect to embryonic patterns). Second, that this would include a significant loss of coordination across the body, in which different tissues ended up phenocopying the transcriptional states of different positions across phylogenetic history, similarly to what occurs in cancer.

[0058] Atavistic Dysregnlation Hypothesis of Aging

[0059] We tested this hypothesis using a meta-phylostratigraphic analysis. We applied a phylostratigraphic analysis using RNA-seq and scRNA-seq data from 8 different studies and two meta-analyses including different tissues RNA-seq during aging, scRNA-seq data from skin cells, an aging meta-analysis of the scRNA-seq of senescent cells, and brain (cortex, hippocampus and cerebellum cells), immune, ovarian and stem cells RNA-seq (see Table 1). We found: (1) An atavistic over-representation of differential expression in the most ancient genes and under-representation in the evolutionary youngest genes for two multi-tissue aging databases, and tissues covering skin, ovarian, immune, senescent and mesenchymal-senescent cells; (2) No significant atavistic over-representation of the differential gene expression during aging of brain cells and mesenchymal stem cells; (3) overall age-dependent increase of heterogeneity in the direction of the phylogenetic position of tissues' transcriptional profiles; and (4) an overall negative evolutionary age mean shift toward the most ancient genes. Thus, we propose that while cancer is a loss of cellular organization in space, aging involves a loss of cellular organization in (evolutionary) time.Dataset Mann-Whitney / ?- value Significance Mean age shiftGenAge 1.052e-36AgeMeta 1.134e-12Skin_40-69 1.577e-18Skin_70+ 1.577e-03T002910Quarles 166118.01584Dataset Mann-Whitney / ?- value Significance Mean age shiftOvary 3.098e-59Progenitors 3.106e-20Mesenchymal_senescent 2.276e-43CellAge_Senescence 3.684e-40Brain Cortex 3.629e-04Brain Hippocampus 9.120e-02 ns -0.31Brain Cerebellum 4.469e-04 *** > Q gMesenchymal 5.313e-01 ns -0.27CD8T 7.218e-ll *** _i 47

[0060] Table 1 : Mann-Whitney U test results for combined gene sets across multiple aging datasets. Note: / ?- values indicate the significance of distribution differences between gene sets and the baseline evolutionary age distribution. Mean age shift represents the average change in evolutionary age categories, where negative values indicate a shift toward older evolutionary ages. Significance levels: ***p < 0.001, **p < 0.01. Bold text indicates significant results (p<0.05).

[0061] Materials and Methods

[0062] We applied a meta-phylostratigraphic analysis using RNA-seq and scRNA-seq data from 8 different studies two meta-analyses including different tissues: RNA-seq during aging, scRNA-seq data from skin cells, an aging meta-analysis of the scRNA-seq of senescentT002910Quarles 166118.01584 cells, and brain (cortex, hippocampus and cerebellum cells), immune, ovarian, and stem cells RNA-seq (see Table 2).

[0063] Table 2: all datasets used in examples for the phylostratigraphic meta-analysis of aging genetic expression.

[0064] To analyze the differentially expressed genes (DEGs) in the aging cells under various conditions through phylostratigraphic analysis, we used the evolutionary ages of 19,660 human protein-coding genes as determined by Litman and Stein (2019). These genes wereT002910Quarles 166118.01584 categorized into 19 major phylostrata, as outlined by Domazet-Loso and Tautz (2010). The phylostrata are a hierarchical range of evolutionary origins for: all living organisms (including Eubacteria, Bacteria and their descendants, e.g., unicellulars), Eukaryota, Opisthokonta, Holozoa, Metazoa, Eumetazoa, Bilateria, Deuterostomia, Chordata, Olfactores, Craniata, Euteleostomi, Tetrapoda, Amniota, Mammalia, Boreoeutheria, Eutheria, Euarchontoglires, and Primates.

[0065] We quantified the number of genes expressed under the various experimental conditions to assess the atavistic patterns we may find during aging in terms of genetic expression. We then applied an overrepresentation test for each group of genes in each phylostrata compared to the ensemble of ages of all human protein-coding genes. The overrepresentation test (also known as enrichment analysis) is a widely used statistical method to determine whether a specific set of elements (e.g., genes, proteins, or other features) is significantly over-represented in a given category compared to a background distribution. Overrepresentation tests are extensively used in various scientific fields, particularly in Gene Ontology (GO) Enrichment Analysis where we identify functional categories over-represented in a set of differentially expressed genes, Pathway Enrichment Analysis where we assess whether biological pathways, such as KEGG pathways, are significantly enriched in a dataset and Disease Association Studies where we link genetic variants to diseases by testing for enrichment in disease-associated categories. This approach is crucial for understanding biological processes, molecular pathways, and functional annotations associated with high-throughput data.

[0066] More specifically, in our case, given a “gene universe” (here the list of all human protein-coding genes) containing N total genes, this test evaluates whether a specific phylostratigraphic category is statistically overrepresented within a smaller subset of n genes selected from this gene universe in a specific condition. With AT genes belonging to a specific phylostratigraphic category in the gene universe, the test calculates the probability of observing k genes from this functional category within the selected subset of n genes randomly, using the hypergeometric distribution:T002910Quarles 166118.01584

[0068] A / ?- value is then calculated to determine the significance of the overrepresentation (X > k) and under-representation (X < k) of the observed enrichment. Here we use a / ?-value inferior to 0.05 to determine the significance, with Benjamini-Hochberg False Discovery Rate (FDR) correction applied to control for multiple testing across all 19 evolutionary age strata within each dataset. We also performed a Mann- Whitney test to assess mean evolutionary age shifts in the overall distribution of evolutionary ages compared to the baseline distribution.

[0069] Results

[0070] Atavistic Over-Representation in the Most Ancient Genes for Multi-Tissues Aging Signatures (GenAge and AgeMeta) Skin, Ovarian, Immune, Senescent, and Mesenchymal-Senescent Cells

[0071] We applied the phylostratigraphic analysis on two multi-tissue aging datasets. The first signature was extracted from GenAge and has been extracted from 127 publicly available microarray and RNA-Seq datasets from mice, rats and humans, identifying a transcriptomic signature of aging across species and tissues (brain, heart and muscle). The second one, AgeMeta, is likewise a meta-analysis of 51 humans scRNA-seq datasets of different tissues (vastus lateralis, cerebellum, frontal cortex, muscle, brain, adipose tissue, adrenal gland, blood, blood vessel, brain (without cerebellum), esophagus, heart, lung, nerve, pituitary, salivary gland, prostate, testis, and thyroid). We observed in both aging signatures an over-representation of the most ancient genes in the “All living organisms” strata (see FIGS. 1 A-1B). The GenAge aging signature shows more over-representations, notably in Ospithokonta, Holozoa, Eumetazoa, Bilateria and Euteleostomi strata while the human AgeMeta signature shows only one overrepresentation in the unicellular genes. The number of upregulated and downregulated genes in the most ancient genes in the GenAge aging signature is almost equal, suggesting more a reshuffling in the genetic expression during aging in the most ancient genes. The AgeMeta aging signature is dominated by downregulated genes in the “All living organisms” strata.

[0072] Interestingly, we also found an under-representation in the evolutionary youngest genes for the GenAge signature for Mammalia, Boroeutheria, Euarchontoglires, and Primates. Similarly, we also observed an important under-representation for the AgeMeta signature in Boroeutheria and Primates strata. Overall, the most important changes were noted in the mostT002910Quarles 166118.01584 ancient genes, while we observed an under-representation of the changes in the youngest ones compared to the expected distribution.

[0073] Both meta-signatures showed significant shifts toward evolutionarily older genes, with mean age shifts of -2.41 and -2.32 categories respectively (see Table 1).

[0074] We conclude that there is an atavistic over-representation of differentially expressed genes in both aging signatures with a heterogeneity in the direction of the genetic changes.

[0075] Atavistic Over -Representation During Aging in Skin, Ovarian, Immune, Senescent, and Mesenchymal-Senescent C lls

[0076] We applied the phylostratographic analysis to skin, ovarian and immune scRNA-seq. For skin cells, in both age groups, gene expression changes are most significant in the most ancient evolutionary category, for example, “All living organisms”. A shift is observed in gene percentages across aging, with a decrease in downregulated genes in the oldest group. In the 40-70 years age group, genes belonging to “All living organisms” show the highest number of expression changes, with a stronger contribution from downregulated genes. In individuals older than 70, the “All living organisms” and “Euteleostomi” categories showed a significant increase in gene expression changes, particularly upregulation for the latter. A notable decrease in gene expression changes is seen in “All living organisms” in the > 70 group compared to the 40-70 group of fibroblasts with < 35% of the changes in this stratum (see FIGS. 2A-2E). The underrepresentation is more widespread for the skin cells. In the 40-70 group of fibroblasts, we have a significant under-representation for “Eukaryota”, “Bilateria”, “Euteleostomi”, “Boroeutheria” and “Primates”. We have an under-representation for the > 70 group only for “Eukaryota” and “Primates”.

[0077] As in skin cells, the highest number of over-represented differentially expressed genes in immune cells was found in “All living organisms”. Compared to skin cells 40-70 group, immune cells exhibited fewer gene expression changes in the most ancient genes. Upregulated genes were more important in the most ancient genes (see FIGS. 2A-2E). We found an underrepresentation for “Eumetazoa”, “Chordata”, “Mammalia”, “Boroeutheria”, “Euarchontoglires”, and “Primates”.T002910Quarles 166118.01584

[0078] Ovarian cells showed the highest total number of differentially expressed genes among all cell types. The most significant gene expression changes occurred in ancient evolutionary categories. From “All living organisms” to “Bilateria” strata, we observed an overrepresentation of the genetic changes (see FIGS. 2A-2E). Ovarian cells also showed a higher level of gene expression shifts (— 1.96, see Table 1), possibly reflecting the pronounced effects of aging in reproductive tissues. The under-representation is clearly located in the youngest genes, from “Tetrapoda” to “Primates” strata.

[0079] Human progenitor cells showed a significant atavistic over-representation in the “All living organisms” evolutionary age (see FIGS. 2A-2E). We observed mostly downregulation in the genetic expression in this over-representation. As in ovarian cells, the underrepresentation is located in the evolutionary youngest genes, from “Mammalia” and “Primates”.

[0080] In the CellAge senescent signature, gene expression changes are most pronounced in ancient evolutionary categories, with “All living organisms” showing the highest level of differentially expressed genes, dominated by downregulation. The stratum “Eukaryota” also exhibits significant over-representation (see FIGS. 3A-3B). The under-representation is also largely located in the youngest genes, from “Tetrapoda” and “Primates” strata.

[0081] Mesenchymal senescent cells followed a similar trend, with “All living organisms” stratum showing significant over-representation, though the magnitude is slightly higher than in the general senescent signature (see FIGS. 3A-3B). We found an underrepresentation mainly located in the youngest genes, from “Amniota” to “Primates”.

[0082] Tissue-specific aging signatures exhibited significant negative evolutionary age shifts: skin cells 40-70 (-2.23), ovary (-1.96), and progenitor cells (-1.84) showed strong shifts toward older genes, while CD8+ T-cells (_1.47) and skin > 70 (-0.77) displayed a more moderate atavistic pattern, indicating tissue-specific manifestations of evolutionary regression during aging (see Table 2). Senescence models (Mesenchymal senescent, CellAge senescence) gene sets demonstrated consistent shifts toward evolutionarily older genes (-2.41 and -2.32 categories).

[0083] Overall, we conclude that there is an atavistic overrepresentation in the differential genetic expression during aging in skin, immune, ovarian and human progenitor cells associated with a heterogeneity in the direction of the changes; sometimes the atavistic change isT002910Quarles 166118.01584 more about downregulation (skin cells), sometimes the opposite with more upregulation in the most ancient genes (immune and ovarian cells).

[0084] No Atavistic Genetic Over-Representation During Aging in Brain Cells and Mesenchymal Stem Cells

[0085] Cerebellum, hippocampus and cortex cells exhibited similar patterns, with an over-representation of differentially expressed genes occurring in the “Euteleostomi” strata. This category showed mainly upregulation for the hippocampus, and more downregulation for the two other types of brain cells, suggesting increased reliance on vertebrate-specific pathways during aging for the brain. There is no genetic atavistic over-representation; the DEGs are indeed not significantly over-represented in the “All living organisms” stratum (FIGS. 4A-4D). The three types of cells showed under-representation mostly located in the evolutionary youngest genes, from “Amniota” to “Primates” for Cerebellum cells, from “Euarchontoglires” to “Primates” strata for hippocampus cells and from “Mammalia” to “Primates” for cortex cells.

[0086] The mesenchymal cells, in contrast, showed no over-representation nor underrepresentation of DEGs in “Euteleostomi” (FIGS. 4A-4D). However, we note that the number of genes analyzed in mesenchymal cells is almost an order of magnitude smaller than that of the other cells. This could have reduced the statistical power to detect potential differences according to evolutionary age.

[0087] Brain aging signatures showed moderate evolutionary age shifts. It is significant for cortex and cerebellum cells, with a mean age shift of -0.74 and -0.90 categories respectively and not significant for the hippocampus with a mean age shift of -0.31 (see Table 1), while mesenchymal stem cells showed a non-significant and minimal shift (-0.27 categories), revealing tissue-specific manifestations of the atavism theory across different cell types.

[0088] We can conclude there is no atavistic over-representation of the genetic changes in the most ancient genes for brain and mesenchymal stem cells suggesting a differential aging or resistance to aging for the brain tissue and stem cells.

[0089] A tavistic Dysregulation of the Most Ancient Genes During Aging

[0090] Overall, we found an age-dependent increase in heterogeneity in the direction of the phylogenetic position of tissues' transcriptional profiles. The number of upregulated andT002910Quarles 166118.01584 downregulated genes in the most ancient genes in the GenAge aging signature is almost equal, suggesting more of a reshuffling in genetic expression during aging in the most ancient genes.

[0091] The AgeMeta aging signature is dominated by downregulated genes in the “All living organisms” strata. Upregulated genes were more important in the most ancient genes in immune and ovarian cells, but not in HPC, senescent or skin cells where downregulated genes dominated in the most ancient genes. For the other types of cells, it is almost equal between up-and downregulated genes in the most ancient genes category.

[0092] Therefore, our analyses suggest that aging involves not merely a stochastic decay but a heterogeneous cellular phylogenetic dissociation over time (see Appendix A for the significant differences using a hypergeometric test between cumulative percentage distributions of gene evolutionary ages for aging-related gene sets compared to the baseline genome distribution).

[0093] Discussion

[0094] We tested predictions of the Atavistic Gene Expression Dissociation (AGED) hypothesis during aging using a meta-phylostratigraphic analysis. We applied it to RNA-seq and scRNA-seq data from 8 different studies two meta-analyses including different tissues: RNA-seq during aging, scRNA-seq data from skin cells, an aging meta-analysis of the scRNA-seq of senescent cells, and brain (cortex, hippocampus and cerebellum cells), immune, ovarian, and stem cells RNA-seq (see Table f ). We found: (f ) An atavistic over-representation of differential expression in the most ancient genes and under-representation in the evolutionary youngest genes for two multi-tissue aging databases, and tissues covering skin, ovarian, immune, senescent and mesenchymal-senescent cells; (2) No significant atavistic over-representation of the differential gene expression during aging of brain cells and mesenchymal stem cells; (3) an overall age-dependent increase of heterogeneity in the direction of the phylogenetic position of tissues' transcriptional profiles; and (4) and an overall negative evolutionary age mean shift toward the most ancient genes. Our analyses suggest that aging involves uncoordinated and tissue-specific phylogenetic changes in gene expression, revealing a cellular dissociation in phylogenetic space during aging resembling the atavistic theory of cancer for aged cells and some tissue and loss of cellular identity. For the last study, they focus on the loss of cellular identity and transcriptomic changes during aging but lack the phylostratigraphic analysis we didT002910Quarles 166118.01584 in this work. We also found some genetic heterogeneity in the directions of the genetic changes as sometimes it is more upregulation of the most ancient genes that predominates and sometimes it is the opposite, and some cells seem not affected by these atavistic changes (brain and mesenchymal stem cells).

[0095] Numerous properties (biochemical, bioelectrical, and biomechanical) are likely affected by cells' phylogenetic status; we focused on transcriptomic analysis as expressed genes provide the only known effective method to estimate the effective phylogenetic age of a given tissue. However, it will be interesting in the future to see whether for example bioelectrical states can also be used to estimate phylogenetic position (i.e., it's not all about the genes).

[0096] To analyze the DEGs in the aging cells under various conditions through phylostratigraphic analysis, we used the evolutionary ages of 19,660 human protein-coding genes as determined by Litman and Stein (2019). These genes were categorized into 19 major phylostrata, as outlined by Domazet-Loso and Tautz (2010). Phylostratigraphic analysis, while providing valuable insights into the evolutionary age of genes, is subject to several limitations that impact the accuracy and consistency of its findings. One challenge is ensuring consistency in ortholog identification across different databases. Litman and Stein (2019) found a consensus across several databases but for some genes, uncertainty may remain, leading to the possibility of both false positives and negatives. Another limitation is analyzing noncoding genes.Phylostratigraphic analysis on these genes is more challenging as a significantly lower fraction of noncoding genes can be assigned a reliable evolutionary age. This discrepancy may come from the rapid divergence and lineage-specific nature of many noncoding sequences, which results in a lower likelihood of detecting homologs across distant taxa. The absence of a consistent modal value for many noncoding genes further underscores the challenge of dating their origins, necessitating reliance on statistical estimates such as the median, which may still introduce uncertainties. In this paper, we didn't analyze the evolutionary age of the noncoding genes and focused on the protein-coding ones. The majority of noncoding genes analyzed in prior studies appear prominently from the Euteleostomi phylostratigraphic age, with the largest expansion observed during the Primata age. This suggests that noncoding genes likely play critical roles in lineage-specific regulatory innovations. Future research addressing noncoding genes would be valuable, providing deeper insights into their evolutionary trajectories andT002910Quarles 166118.01584 functional implications in various lineages, particularly within the “Primata” phylostratographic age, where their abundance is highest.

[0097] Senescent behavior is not restricted to proliferative arrest. Indeed, another important characteristic of senescent cells is the senescence-associated secretory phenotype (SASP), where cells secrete inflammatory cytokines, growth factors, and proteases into their environment. From an atavistic point of view, cellular senescence may not be just a passive response to molecular damage but an active, evolutionarily conserved process that occurs when the controls of multicellularity fail and cells pursue their own goals, and that plays a role in cellular interaction and adaptation to high-stress conditions. However, in the case of aging, this process is dysregulated and leads to a defect of tissue homeostasis and promotes chronic inflammation.

[0098] Heterochronic parabiosis, the surgical connection of the circulatory systems of young and old organisms, has demonstrated that aging is not irreversible but is instead highly plastic and responsive to systemic factors. From the perspective of the atavistic theory of aging, the rejuvenating effects observed in heterochronic parabiosis experiments may not come from the inhibition or reduction of pro-aging factors along with the introduction of youthful signals, but rather from the reconstitution of the multicellular integration that has been lost and the reduction of cellular phylogenetic dissociation. If aging is a trajectory of increasing cellular atavism, where cells revert to evolutionarily ancient behaviors, then exposure to young systemic factors may act as a re-establishment of developmental / morphogenetic constraints, pulling aged cells back into the organizational structure of a more coherent, goal-directed system.

[0099] Cancer can be understood as the failure of the computational boundary that regulates organ-level integration in the body. The cells lose multicellular integration into networks that pursue large-scale anatomical setpoints, and function more as autonomous units that treat the rest of the body as external environment, similarly to their ancestral unicellular behaviors. Therefore, cancer is believed to occur due to failure of the regulatory networks that usually restrict individual-level goals and favor morphogenetic goals such as bioelectric signaling, resulting in uncontrolled cellular autonomy and proliferation. This reduction of the radius of cooperation / coordination among cells, from organ-level to individual cell level, is consistent with the loss of phylogenetic age we observed across tissues with age.T002910Quarles 166118.01584

[0100] In the atavistic model of cancer, cancer progression is seen as an expression of the genetic to the ancestral cellular states, which are defined by the reactivation of the genetic programs that have been inherited from the unicellular or early multicellular evolutionary stages. While the somatic mutation theory (SMT) has been used to explain the development of cancer as a result of sequential mutations and natural selection events within the human body, the atavistic model is based on the idea that ancient functionalities that were essential for early and unicellular life forms are involved in the disease. These functionalities are uncontrolled growth, lack of response to growth inhibitors and increased stress tolerance. Phylostratigraphic analysis has been used to establish that many genes associated with cancer were born during critical transitions in the evolution of life on Earth such as the rise of unicellular, eukaryotes or multicellularity.

[0101] Also, in the active inference framework, cells are described as having a model of themselves and the outside world. Maybe evolutionary stage is part of their self-model, and the transcriptional changes we see are a readout of the change of this model with age.

[0102] Atavistic dysregulation during aging suggests new avenues for longevity therapeutics: strategies should focus on restoring and reinforcing multicellular coherence, reestablishing a multi-cellular and evolutionary younger genetic expression that preserves tissue and organ integration (perhaps by stimuli that remind cells of their modern evolutionary status). In the context of aging as a loss of morphostatic information and goal-directedness, cellular dissociation in morphogenetic space suggests that giving evolutionarily recent information for the organism may counteract aging. We suggest this is the beginning of a roadmap that pursues healthspan and longevity by managing the processing, by cells and tissues, of information across a wide range of temporal and spatial scales.

[0103] Further Analysis

[0104] FIGS. 5A-5M show cumulative percentage distributions of gene evolutionary ages for aging-related gene sets (blue lines) compared to the baseline genome distribution (orange lines). Red stars indicate significant over-representation and black stars indicate significant under-representation at each evolutionary age cutoff (FDR-corrected p < 0.05, cumulative hypergeometric test).

[0105] References for Example 1T002910Quarles 166118.01584

[0106] 1. Ashburner, M. , Ball C. A., Blake J. A., et al. 2000. “Gene Ontology: Tool for the Unification of Biology. The Gene Ontology Consortium.” Nature Genetics 25, no. 1: 25-29.

[0107] 2. Austad, S. N. , and Hoffman J. M.. 2018. “Is Antagonistic Pleiotropy Ubiquitous in Aging Biology?” Evolution, Medicine, and Public Health 2018, no. 1 : 287-294.

[0108] 3. Avelar, R. A. , Ortega J. G., Tacutu R., et al. 2020. “A Multidimensional Systems Biology Analysis of Cellular Senescence in Aging and Disease.” Genome Biology 21, no. 1: 91.

[0109] 4. Bussey, K. J. , Cisneros L. H., Lineweaver C. H., and Davies P. C. W.. 2017. “Ancestral Gene Regulatory Networks Drive Cancer.” Proceedings of the National Academy of Sciences of the United States of America 114, no. 24: 6160-6162.

[0110] 5. Campisi, J. 2013. “Aging, Cellular Senescence, and Cancer.” Annual Review of Physiology 75, no. 1: 685-705.

[0111] 6. Chernet, B. T. , and Levin M.. 2013. “Endogenous Voltage Potentials and the Microenvironment: Bioelectric Signals That Reveal, Induce and Normalize Cancer.” Journal of Clinical & Experimental Oncology Suppl 1: Sl-002.

[0112] 7. Conboy, I. M. , Conboy M. J., Wagers A. J., Girma E. R., Weissman I. L., and Rando T. A.. 2005. “Rejuvenation of Aged Progenitor Cells by Exposure to a Young Systemic Environment.” Nature 433, no. 7027: 760-764.

[0113] 8. Davies, P. C. , and Lineweaver C. H.. 2011. “Cancer Tumors as Metazoa 1.0: Tapping Genes of Ancient Ancestors.” Physiological Biology 8, no. 1: 015001.

[0114] 9. de Magalhaes, J. P. 2011. “The Biology of Ageing: A Primer.” In An Introduction to Gerontology, edited by Stuart-Hamilton I., 21-47. Cambridge University Press.

[0115] 10. de Magalhaes, J. P. 2023. “Ageing as a Software Design Flaw.” Genome Biology 24, no. 1 : 51.

[0116] 11. de Magalhaes, J. P , and Church G. M.. 2005. “Genomes Optimize Reproduction: Aging as a Consequence of the Developmental Program.” Physiology (Bethesda) 20, no. 4: 252-259.T002910Quarles 166118.01584

[0117] 12. Domazet-Loso, T. , Brajkovic J., and Tautz D.. 2007. “A Phylostratigraphy Approach to Uncover the Genomic History of Major Adaptations in Metazoan Lineages.” Trends in Genetics 23, no. 11: 533-539.

[0118] 13. Domazet-Loso, T. , and Tautz D.. 2010. “Phylostratigraphic Tracking of Cancer Genes Suggests a Link to the Emergence of Multicellularity in Metazoa.” BMC Biology 8: 66.

[0119] 14. Duran-Nebreda, S. , Bonforti A., Montanez R., Valverde S., and Sole R.. 2016. “Emergence of Proto-Organisms From Bistable Stochastic Differentiation and Adhesion.” Journal of the Royal Society Interface 13, no. 117: 20160108.

[0120] 15. Fields, C. , and Levin M.. 2022. “Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments.” Entropy (Basel) 24, no. 6: 819.

[0121] 16. Friston, K. 2012. “AFree Energy Principle for Biological Systems.” Entropy (Basel) 14, no. 11: 2100-2121.

[0122] 17. Friston, K. , Levin M., SenguptaB., and Pezzulo G.. 2015. “Knowing One's Place: A Free-Energy Approach to Pattern Regulation.” Journal of the Royal Society Interface 12, no. 105: 20141383.

[0123] 18. Gems, D. 2022. “The Hyperfunction Theory: An Emerging Paradigm for the Biology of Aging.” Ageing Research Reviews 74: 101557.

[0124] 19. Gilbert, S. F. , Opitz J. M., and RafFR. A.. 1996. “Resynthesizing Evolutionary and Developmental Biology.” Developmental Biology 173, no. 2: 357-372.

[0125] 20. Gladyshev, V. N. , Kritchevsky S. B., Clarke S. G., et al. 2021. “Molecular Damage in Aging.” Nature Aging 1, no. 12: 1096-1106.

[0126] 21. Gonzalez- Velasco, O. , Papy-GarciaD., le Douaron G., Sanchez-Santos J. M., and de Las Rivas J.. 2020. “Transcriptomic Landscape, Gene Signatures and Regulatory Profde of Aging in the Human Brain.” Biochimica et Biophysica Acta (BB A) — Gene Regulatory Mechanisms 1863, no. 6: 194491.T002910Quarles 166118.01584

[0127] 22. Harris, A. K. 2018. “The Need for a Concept of Shape Homeostasis.” Biosystems 173: 65-72.

[0128] 23. Hayflick, L. 2007. “Biological Aging Is No Longer an Unsolved Problem.” Annals of the New York Academy of Sciences 1100, no. 1 : 1-13.

[0129] 24. Izgi, H. , Han D., Isildak U., et al. 2022. “Inter-Tissue Convergence of Gene Expression During Ageing Suggests Age-Related Loss of Tissue and Cellular Identity.” eLife 11 : e68048.

[0130] 25. Jeffery, W. R. , and RafFR. A.. 1983. “Time, Space, and Pattern in Embryonic Development.” In MBL Lectures in Biology, vol. 2, xvii-395. A.R. Liss.

[0131] 26. Jin, C. , Wang X., Yang J., et al. 2024. “Molecular and Genetic Insights Into Human Ovarian Aging From Single-Nuclei Multi-Omics Analyses.” Nature Aging 5: 1-290.

[0132] 27. Kanehisa, M. , Furumichi M., Tanabe M., Sato Y, and Morishima K.. 2017. “KEGG: New Perspectives on Genomes, Pathways, Diseases and Drugs.” Nucleic Acids Research 45, no. DI: D353-D361.

[0133] 28. Khosla, S. , Farr J. N., Tchkonia L, and Kirkland J. L.. 2020. “The Role of Cellular Senescence in Ageing and Endocrine Disease.” Nature Reviews. Endocrinology 16, no.5: 263-275.

[0134] 29. Kirchhoff, M. , Parr T., Palacios E., Friston K., and Kiverstein J.. 2018. “The Markov Blankets of Life: Autonomy, Active Inference and the Free Energy Principle.” Journal of the Royal Society Interface 15, no. 138: 20170792.

[0135] 30. Kirkwood, T. B. , and Melov S.. 2011. “On the Programmed / Non- Programmed Nature of Ageing Within the Life History.” Current Biology 21, no. 18: R701-R707.

[0136] 31. Kuchling, F. , Friston K., Georgiev G., and Levin M.. 2020. “Morphogenesis as Bayesian Inference: A Variational Approach to Pattern Formation and Control in Complex Biological Systems.” Physics of Life Reviews 33: 88-108.T002910Quarles 166118.01584

[0137] 32. Lagasse, E. , and Levin M . 2023. “Future Medicine: From Molecular Pathways to the Collective Intelligence of the Body.” Trends in Molecular Medicine 29: 687-710.

[0138] 33. Levin, M. 2019. “The Computational Boundary of a “Self’: Developmental Bioelectricity Drives Multi cellularity and Scale-Free Cognition.” Frontiers in Psychology 10, no.2688: 2688.

[0139] 34. Levin, M. 2021. “Bioelectrical Approaches to Cancer as a Problem of the Scaling of the Cellular Self.” Progress in Biophysics and Molecular Biology 165: 102-113.

[0140] 35. Levin, M. 2023a. “Collective Intelligence of Morphogenesis as a Teleonomic Process.” In Evolution ‘on Purpose’ : Teleonomy in Living Systems, edited by Corning P A., Kauffman S. A., Noble D., et al., 175-198. MIT Press.

[0141] 36. Levin, M. 2023b. “Bioelectric Networks: The Cognitive Glue Enabling Evolutionary Scaling From Physiology to Mind.” Animal Cognition 26: 1865-1891.

[0142] 37. Levin, M. 2024. “The Multiscale Wisdom of the Body: Collective Intelligence as a Tractable Interface for Next-Generation Biomedicine.” BioEssays 47: e202400196.

[0143] 38. Lidsky, P. V. , and Andino R.. 2020. “Epidemics as an Adaptive Driving Force Determining Lifespan Setpoints.” Proceedings of the National Academy of Sciences of the United States of America 117, no. 30: 17937-17948.

[0144] 39. Lidsky, P. V. , and Andino R.. 2022. “Could Aging Evolve as a Pathogen Control Strategy?” Trends in Ecology & Evolution 37, no. 12: 1046-1057.

[0145] 40. Lidsky, P. V. , Yuan J., and Andino R.. 2023. “Reconsidering Life History Theory Amid Infectious Diseases.” Trends in Ecology & Evolution 38, no. 8: 699-700.

[0146] 41. Lidsky, P. V. , Yuan J., Rulison J. M., and Andino-Pavlovsky R.. 2022. “Is Aging an Inevitable Characteristic of Organic Life or an Evolutionary Adaptation?” Biochemistry (Moscow) 87, no. 12: 1413-1445.

[0147] 42. Lineweaver, C. H. , and Davies P. C. W.. 2021. “Comparison of the Atavistic Model of Cancer to Somatic Mutation Theory: Phylostrati graphic Analyses Support the Atavistic Model.” In The Physics of Cancer: Research Advances, 243-261. World Scientific.T002910Quarles 166118.01584

[0148] 43. Litman, T. , and Stein W. D.. 2019. “Obtaining Estimates for the Ages of All the Protein-Coding Genes and Most of the Ontology-Identified Noncoding Genes of the Human Genome, Assigned to 19 Phylostrata.” Seminars in Oncology 46: 3-9.

[0149] 44. Lopez-Otin, C. , Blasco M. A., Partridge L., Serrano M., and Kroemer G.. 2023. “Hallmarks of Aging: An Expanding Universe.” Cell 186, no. 2: 243-278.

[0150] 45. Lu, J. , Ahmad R., Nguyen T., et al. 2022. “Heterogeneity and Transcriptome Changes of Human CD8(+) T Cells Across Nine Decades of Life.” Nature Communications 13, no. 1: 5128.

[0151] 46. McGhee, G. R. 2007. The Geometry of Evolution: Adaptive Landscapes and Theoretical Morphospaces, xii-200. Cambridge University Press.

[0152] 47. McMillen, P. , and Levin M.. 2024. “Collective Intelligence: A Unifying Concept for Integrating Biology Across Scales and Substrates.” Communications Biology 7, no.1: 378.

[0153] 48. Moore, D. , Walker S. I., and Levin M.. 2017. “Cancer as a Disorder of Patterning Information: Computational and Biophysical Perspectives on the Cancer Problem.” Convergent Science Physical Oncology 3: 043001.

[0154] 49. Newman, S. A. , and Bhat R.. 2009. “Dynamical Patterning Modules: A ‘Pattern Language’ for Development and Evolution of Multicellular Form.” International Journal of Developmental Biology 53, no. 5-6: 693-705.

[0155] 50. Nie, C. , Li Y, Li R., et al. 2022. “Distinct Biological Ages of Organs and Systems Identified From a Multi-Omics Study.” Cell Reports 38, no. 10: 110459.

[0156] 51. Nijenhuis, E. R. , Spinhoven P, van Dyck R., van der Hart O., and Vanderlinden J.. 1998. “Degree of Somatoform and Psychological Dissociation in Dissociative Disorder Is Correlated With Reported Trauma.” Journal of Traumatic Stress 11, no. 4: 711-730.

[0157] 52. Olle-Vila, A. , Duran-Nebreda S., Conde-Pueyo N., Montanez R , and Sole R .2016. “ A Morphospace for Synthetic Organs and Organoids: The Possible and the Actual.” Integrative Biology 8, no. 4: 485-503.T002910Quarles 166118.01584

[0158] 53. Palmer, D. , Fabris F., Doherty A., Freitas A. A., and de Magalhaes J. P. 2021. “Ageing Transcriptome Meta- Analysis Reveals Similarities and Differences Between Key Mammalian Tissues.” Aging (Albany NY) 13, no. 3: 3313-3341.

[0159] 54. Parr, T. , Pezzulo G., and Friston K. J.. 2022. Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. MIT Press.

[0160] 55. Partridge, L. , Deelen J., and Slagboom P. E.. 2018. “Facing up to the Global Challenges of Ageing.” Nature 561, no. 7721: 45-56.

[0161] 56. Pezzulo, G. , and Levin M.. 2015. “Re-Membering the Body: Applications of Computational Neuroscience to the Top-Down Control of Regeneration of Limbs and Other Complex Organs.” Integrative Biology 7, no. 12: 1487-1517.

[0162] 57. Pezzulo, G. , and Levin M.. 2016. “Top-Down Models in Biology:Explanation and Control of Complex Living Systems Above the Molecular Level.” Journal of the Royal Society Interface 13, no. 124: 20160555.

[0163] 58. Pio-Lopez, L. , Hartl B., and Levin M.. 2025. “Aging as a Loss of Goal- Directedness: An Evolutionary Simulation and Analysis Unifying Regeneration with Anatomical Rejuvenation.” Advanced Science: e09872. 10.1002 / advs.202509872.

[0164] 59. Pio-Lopez, L. , Kuchling F., Tung A., Pezzulo G., and Levin M.. 2022.“Active Inference, Morphogenesis, and Computational Psychiatry.” Frontiers in Computational Neuroscience 16: 988977.

[0165] 60. Pio-Lopez, L. , and Levin M.. 2024. “Aging as a Loss of Morphostatic Information: A Developmental Bioelectricity Perspective.” Ageing Research Reviews 97:102310.

[0166] 61. Raff, R. A. 1996. The Shape of Life: Genes, Development, and the Evolution of Animal Form, xxiii-520. University of Chicago Press.

[0167] 62. Rasskin-Gutman, D. , and Izpisua-Belmonte J. C.. 2004. “Theoretical Morphology of Developmental Asymmetries.” BioEssays 26, no. 4: 405-412.

[0168] 63. Rubin, H. 1985. “Cancer as a Dynamic Developmental Disorder.” Cancer Research 45, no. 7: 2935-2942.T002910Quarles 166118.01584

[0169] 64. Rubin, H. 2006. “What Keeps Cells in Tissues Behaving Normally in the Face of Myriad Mutations?” BioEssays 28, no. 5: 515-524.

[0170] 65. Rubin, H. 2007. “Ordered Heterogeneity and Its Decline in Cancer and Aging.” Advances in Cancer Research 98: 117-147.

[0171] 66. Rubin, H. , Chow M., and Yao A.. 1996. “Cellular Aging, Destabilization, and Cancer.” Proceedings of the National Academy of Sciences of the United States of America 93, no. 5: 1825-1830.

[0172] 67. Schaum, N. , Lehallier B., Hahn O., et al. 2020. “Ageing Hallmarks Exhibit Organ-Specific Temporal Signatures.” Nature 583, no. 7817: 596-602.

[0173] 68. Skulachev, M. V. , and Skulachev V. P. 2014. “New Data on Programmed Aging — Slow Phenoptosis.” Biochemistry (Moscow) 79: 977-993.

[0174] 69. Soto, A. M. , and Sonnenschein C.. 2004. “The Somatic Mutation Theory of Cancer: Growing Problems With the Paradigm?” BioEssays 26, no. 10: 1097-1107.

[0175] 70. Stone, J. R. 1997. “The Spirit of D'arcy Thompson Dwells in Empirical Morphospace.” Mathematical Biosciences 142, no. 1: 13-30.

[0176] 71. Subramanian, A. , Tamayo P., Mootha V. K., et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences of the United States of America 102, no. 43: 15545-15550.

[0177] 72. Thomas, F. , Nesse R. M., Gatenby R., et al. 2016. “Evolutionary Ecology of Organs: A Missing Link in Cancer Development?” Trends Cancer 2, no. 8: 409-415.

[0178] 73. Thomas, F. , Ujvari B., Renaud F., and Vincent M.. 2017. “Cancer Adaptations: Atavism, de Novo Selection, or Something in Between?” BioEssays 39, no. 8: 1700039.

[0179] 74. Tikhonov, S. , Batin M., Gladyshev V. N., Dmitriev S. E., and Tyshkovskiy A..2024. “AgeMeta: Quantitative Gene Expression Database of Mammalian Aging.” Biochemistry (Mose) 89, no. 2: 313-321.T002910Quarles 166118.01584

[0180] 75. Wagner, W. , Bork S., Hom P, et al. 2009. “Aging and Replicative Senescence Have Related Effects on Human Stem and Progenitor Cells.” PLoS One 4, no. 6: e5846.

[0181] 76. Waller, G. , Hamilton K., Elliott P., et al. 2001. “Somatoform Dissociation, Psychological Dissociation, and Specific Forms of Trauma.” Journal of Trauma & Dissociation l, no. 4: 81-98.

[0182] 77. Zou, Z. , Long X., Zhao Q., et al. 2021. “A Single-Cell Transcriptomic Atlas of Human Skin Aging.” Developmental Cell 56, no. 3: 383-397. e8.

[0183] Example 2 - Illustrative Embodiments of Methods and Systems Described Herein

[0184] FIG. 6 shows an example process 600 to characterize aging in a subject. At 602, gene expression data collected from a plurality of samples from the subject may be obtained. At 604, a plurality of differentially expressed genes based on the gene expression data may be identified. At 606, an age signature of each of sample in the plurality of samples may be determined based on the plurality of differentially expressed genes. At 608, aging in the subject may be characterized based on the determined age signature of each sample in the plurality of samples.

[0185] In FIG. 7, an example 700 of a system (e.g., a data processing system) for characterizing a protein in accordance with some embodiments of the disclosed subject matter is shown.

[0186] In some embodiments, computing device 704 and / or server 716 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, etc. As described herein, system 700 can present information about characterized aging of a sample to a user (e.g., a researcher and / or a physician).

[0187] In some embodiments, communication network 702 can be any suitable communication network or combination of communication networks. In some embodiments, communication network 702 can be any suitable communication network or combination of communication networks. For example, communication network 702 can include a Wi-FiT002910Quarles 166118.01584 network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 4G network, a 5G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, etc. In some embodiments, communication network 702 can be a local area network, a wide area network, a public network (e g., the Internet), a private or semiprivate network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in FIG. 7 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, etc.

[0188] FIG. 7 additionally shows an example of hardware that can be used to implement computing device 704 and server 716 in accordance with some embodiments of the disclosed subject matter. In some embodiments, computing device 704 can be used to execute one or more set of instructions to characterize aging in a subject. In other embodiments, computing device 704 can be used to predict disease based on the characterized age of a sample.

[0189] As shown in FIG. 7, computing device 704 can include one or more hardware processor 706, one or more displays 708, one or more inputs 710, one or more communications 712, and / or memory 714. In some embodiments, processor 706 can be any suitable hardware processor or combination of processors, such as central processing unit, a graphics processing unit, etc. In some embodiments, display 708 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, etc. In some embodiments, inputs 710 can include any suitable input device and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, etc.

[0190] In some embodiments, communication systems 712 can include any suitable hardware, firmware, and / or software for communicating information over communication network 702 and / or any other suitable communication networks. For example, communications systems 712 can include one or more transceivers, one or more communication chips and / or chip sets, etc. In a more particular example, communications systems 712 can include hardware, firmware and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, etc.T002910Quarles 166118.01584

[0191] In some embodiments, memory 714 can include any suitable storage device or devices that can be used to store instructions, values, etc., that can be used, for example, by processor 706 to present content using display 708, to communicate with server 716 via communications system(s) 712, etc.

[0192] Memory 714 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 714 can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, etc. In some embodiments, memory 714 can have encoded thereon a computer program for controlling operation of computing device 704. In such embodiments, processor 706 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables, etc.), receive content from server 716, transmit information to server 716, etc.

[0193] In some embodiments, server 716 can include a processor 718, a display 720, one or more inputs 722, one or more communications systems 724, and / or memory 726. In some embodiments, processor 718 can be any suitable hardware processor or combination of processors, such as a 720 processing unit, a graphics processing unit, etc. In some embodiments, display 720 can include any suitable display devices, such as a computer monitor, a touchscreen, a television, etc. In some embodiments, inputs 722 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, etc.

[0194] In some embodiments, communications systems 724 can include any suitable hardware, firmware, and / or software for communicating information over communication network 702 and / or any other suitable communication networks. For example, communications systems 724 can include one or more transceivers, one or more communication chips and / or chip sets, etc. In a more particular example, communications systems 724 can include hardware, firmware and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, etc.

[0195] In some embodiments, memory 726 can include any suitable storage device or devices that can be used to store instructions, values, etc., that can be used, for example, by processor 718 to present content using display 720, to communicate with one or more computingT002910Quarles 166118.01584 devices 704, etc. Memory 726 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 726 can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, etc. In some embodiments, memory 726 can have encoded thereon a server program for controlling operation of server 716. In such embodiments, processor 718 can execute at least a portion of the server program to transmit information and / or content (e.g., results of characterization of a sample, a user interface, etc.) to one or more computing devices 704, receive information and / or content from one or more computing devices 704, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), etc.

[0196] In some embodiments, any suitable computer readable media can be used for storing instructions for performing the functions and / or processes described herein. For example, in some embodiments, computer readable media can be transitory or non-transitory. For example, non-transitory computer readable media can include media such as magnetic media (such as hard disks, floppy disks, etc.), optical media (such as compact discs, digital video discs, Blu-ray discs, etc.), semiconductor media (such as RAM, Flash memory, electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), etc.), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and / or any suitable tangible media. As another example, transitory computer readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and / or any suitable intangible media.

[0197] A number of references to patent and non-patent documents are made throughout the publication, each of which is herein incorporated by reference in its entirety.

[0198] While the invention has been described above in connection with particular embodiments and examples, the invention is not necessarily so limited, and that numerous other embodiments, examples, uses, modifications and departures from the embodiments, examples and uses are intended to be encompassed by the claims attached hereto.

Claims

T002910Quarles 166118.01584 CLAIMSWhat is claimed is:

1. A method of characterizing aging in a subject, comprising:obtaining gene expression data collected from a plurality of samples from the subject; identifying a plurality of differentially expressed genes based on the gene expression data;determining an age signature of each sample in the plurality of samples based on the plurality of differentially expressed genes;characterizing aging in the subject based on the determined age signature of each sample in the plurality of samples.

2. The method of claim 1, wherein the age signature comprises at least one transcriptomic signature of aging.

3. The method of claim 1, wherein characterizing aging in the sample further comprises performing enrichment analysis on the identified plurality of differentially expressed genes.

4. The method of claim 3, wherein enrichment analysis comprises:selecting a subset of genes with a shared aging signature;calculating the probability of observing the subset of genes with the shared aging signature relative to a randomly selected subset of genes.

5. The method of claim 1, wherein characterizing aging in the subject comprises quantifying differences in the age signatures of each sample in the plurality of samples.T002910Quarles 166118.01584 6. The method of claim 5, wherein quantifying differences in age signatures comprises at least one of calculating a distribution, range, interquartile range, variance, standard deviation, or mean absolute deviation of the age signature of each sample in the plurality of samples.

7. The method of claim 1, wherein characterizing aging in the subject further comprises comparing characterized aging relative to a control population comprising a plurality of control subjects.

8. The method of claim 7, wherein the plurality of control subjects is selected from a database.

9. The method of claim 5, wherein assessing aging shifts in the sample comprises determining shifts in gene expression across tissue type, cell type, age, species, strains, or treatment status.

10. The method of claim 1, further comprising predicting a disease based on the characterized aging in the sample.

11. The method of claim 10, wherein predicting a disease comprises:determining a control age signature of a control population; andcomparing the control age signature and the age signature of the subject.

12. The method of claim 10 or 11, wherein the disease is at least one of cardiovascular disease, cancer, neurodegenerative disorders, metabolic disorders, immune system disease, or associated with aging.

13. The method of any one of claims 11-16, wherein predicting the disease further comprises:T002910Quarles 166118.01584 obtaining updated gene expression data collected from an updated plurality of samples from the subject;characterizing aging in the updated plurality of samples; andmonitoring changes in the characterized aging in the sample over time,wherein the updated plurality of samples from the subject is collected at least one month after the plurality of samples in claim 1 is collected.

14. The method of claim 1, further comprising screening treatment conditions.

15. The method of claim 14, wherein screening treatment conditions comprises:determining a baseline distribution of aging signatures of subjects that have not been exposed to the treatment condition;comparing the characterized aging of a subject who has been exposed to the treatment condition to the baseline distribution of aging signatures that have not been exposed to the treatment condition.

16. The method of claim 1, further comprising evaluating the effect of a treatment condition, comprising:characterizing aging of the subject prior to exposure to the treatment condition; applying the treatment condition to the subject;characterizing aging of the subject after exposure to the treatment condition; and comparing the aging of the subject prior to and after exposure to the treatment condition.

17. The method of claim 1, wherein the identified plurality of differentially expressed genes comprises protein-encoding genes.T002910Quarles 166118.01584 18. The method of claim 1 , wherein the gene expression data comprises at least one of RNA sequencing data or protein expression data.

19. The method of claim 18, wherein the RNA sequencing data is at least one of RNA-seq data or scRNA-seq data.

20. The method of claim 1, wherein a subject is a human, an animal, a plant, or a synthetic multicellular organism.

21. The method of claim 1, wherein each sample in the plurality of samples is collected from a cell or tissue selected from at least one of skin cells, senescent cells, brain cells, immune cells, ovarian cells, or stem cells.

22. A system for characterizing aging in a subject, comprising:a processor in communication with a memory, the memory having stored thereon a set of instructions which, when executed by the processor, cause the processor to:(a) obtain gene expression data collected from a plurality of samples from the subject;(b) identify a plurality of differentially expressed genes based on the gene expression data; (c) determine an age signature of each sample in the plurality of samples based on the plurality of differentially expressed genes; and(d) characterize aging in the subject based on the determined age signature of each sample in the plurality of samples.

23. The system of claim 22, wherein the age signature comprises at least one transcriptomic signature of aging.T002910Quarles 166118.01584 24. The system of claim 22, wherein characterizing aging further comprises performing enrichment analysis on the identified plurality of differentially expressed genes.

25. The system of claim 24, wherein the instructions further cause the processor to:(a) select a subset of genes having a shared aging signature; and(b) calculate a probability of observing the subset of genes with the shared aging signature relative to a randomly selected subset of genes.

26. The system of claim 22, wherein characterizing aging in the subject comprises quantifying differences in the age signatures of each sample in the plurality of samples.

27. The system of claim 26, wherein quantifying differences in age signatures comprises calculating at least one of a distribution, range, interquartile range, variance, standard deviation, or mean absolute deviation of the age signatures.

28. The system of claim 22, wherein characterizing aging further comprises comparing the characterized aging of the subject to a control population comprising a plurality of control subjects.

29. The system of claim 28, wherein the plurality of control subjects is selected from a database.

30. The system of claim 26, wherein quantifying differences in age signatures comprises determining shifts in gene expression across tissue type, cell type, age, species, strain, or treatment status.

31. The system of claim 22, wherein the instructions further cause the processor to predict a disease based on the characterized aging in the subject.T002910Quarles 166118.0158432. The system of claim 31, wherein predicting the disease comprises:(a) determining a control age signature of a control population; and(b) comparing the control age signature with the age signature of the subject.

33. The system of claim 31, wherein the disease comprises at least one of cardiovascular disease, cancer, neurodegenerative disorders, metabolic disorders, immune system disease, or a disease associated with aging.

34. The system of claim 32, wherein predicting the disease further comprises:(a) obtaining updated gene expression data collected from an updated plurality of samples from the subject;(b) characterizing aging in the updated plurality of samples; and(c) monitoring changes in the characterized aging over time,wherein the updated plurality of samples is collected at least one month after the plurality of samples obtained in claim 22.

35. The system of claim 22, wherein the instructions further cause the processor to screen treatment conditions.

36. The system of claim 35, wherein screening treatment conditions comprises:(a) determining a baseline distribution of aging signatures of subjects not exposed to a treatment condition; and(b) comparing the characterized aging of a subject exposed to the treatment condition with the baseline distribution.T002910Quarles 166118.01584 37. The system of claim 22, wherein the instructions further cause the processor to evaluate an effect of a treatment condition by:(a) characterizing aging of the subject prior to exposure to the treatment condition;(b) characterizing aging of the subject after exposure to the treatment condition; and(c) comparing aging of the subject prior to and after exposure.

38. The system of claim 22, wherein the identified plurality of differentially expressed genes comprises protein-encoding genes.

39. The system of claim 22, wherein the gene expression data comprises at least one of RNA sequencing data or protein expression data.

40. The system of claim 39, wherein the RNA sequencing data comprises at least one of RNA-seq data or single-cell RNA sequencing (scRNA-seq) data.

41. The system of claim 22, wherein the subject is a human, an animal, a plant, or a synthetic multicellular organism.

42. The system of claim 22, wherein each sample in the plurality of samples is collected from a cell or tissue selected from skin cells, senescent cells, brain cells, immune cells, ovarian cells, or stem cells.

Images